Abstract Antibiotics have been considered the single most significant medical discovery of the 20th century. Due to decades of over-prescription and misuse, however, antibiotics are losing their efficacy due to the emergence and rapid rise of antibiotic-resistant bacteria. To combat this imminent threat, in addition to developing new drugs, it is equally critical to develop strategies that enable more effective use of existing antibiotics. Doing so requires a mechanistic understanding of both short-term and long-term bacterial population dynamics in response to antibiotic treatment. An intriguing phenomenon that arises from antibiotic treatment is the post-antibiotic effect (PAE) ? after transient treatment, the growth of a bacterial population is often temporarily suppressed even after the antibiotic is removed. This phenomenon was first described in the 1940s and has since been reported in the majority of, but not all, antibiotics. Despite the prevalence of PAE, however, the underlying mechanisms are poorly understood. The objective of our proposed research is to elucidate the molecular mechanism underlying generation of PAE and, based on this understanding, to design effective antibiotic treatment protocols. In our preliminary work, we measured PAE arising from treatment of E. coli with several well-characterized antibiotics. Based on these measurements and the literature data, we hypothesized that PAE can be explained by the uptake and export kinetics of antibiotics by cells. Our proposed research will examine this hypothesis and its alternatives in depth and breadth. Moreover, we will use computation modeling to design and experimentally test antibiotic protocols to exploit PAE. In particular, we aim to design treatment protocols that achieve similar treatment efficacy while using minimal amounts of drugs. Such protocols will reduce perturbation to the native microbiota and exert less selection pressure that can drive emergence and spread of antibiotic resistance.